The weight status of Americans has been declared a serious public health problem and is already burdening our health care systems. Obesity and low physical activity are prime risks for multiple forms of morbidity and mortality including several chronic diseases (coronary heart disease, Type II Diabetes, arthritis, sleep apnea and some forms of cancer). Obesity and overweight are a result of energy imbalance—excess macronutrient intake and low levels of physical activity.
Unfortunately, past approaches to weight loss treatments have proven ineffective for both children and adults, despite increasing awareness and individual efforts to lose weight. Traditionally effective treatments for overweight individuals need to be intensive enough to cause a change in lifestyle. Techniques to deliver such treatments would overwhelm our healthcare system. Programs also need to reach people with limited finances and who live in locations remote from experienced professionals and clinics. Further, feedback and other support for patients must be adapted to their daily lives and as concurrently as possible with behavior that should be altered.
An exemplary use of the invention is ecological momentary assessment (EMA). EMA is a timely record of actions and environment that preserves immediate psychological and physical status. EMA replaces recall diaries to overcome bias and errors from delayed records. Simple examples of EMA might have a study participant provide a rating of perceived exertion (RPE) after an exercise session or assign a score from a Likert scale to their present mood. Electronic EMA (eEMA) methods are a further improvement that replace paper surveys and increase privacy. EMA has become a popular tool to collect data and influence behavior change. EMA allows researchers and clinicians to obtain behavioral, social context, and individual cognition in near real time. eEMA has been demonstrated as superior to other methods, especially paper, for avoiding recall bias.
Rofey, et al [“Utilizing Ecological Momentary Assessment in pediatric obesity to quantify behavior, emotion, and sleep”, Obesity 18(6):1270-1272, 2010] stated that, “ . . . technological devices that gather objective data have reasonably high compliance rates, and may inform measurement of treatment outcomes in adolescents who are obese”. Mobile devices, especially “smartphones” can enhance eEMA with objective measures made by sensors such as time, location, speed, acceleration, temperature, light level, and proximity to locations of interest or electronic devices (including other smartphones) carried by people with a relation to a participant. These additional objective measures can be attached to a particular instance of eEMA (such as a Survey) to provide context. Or, the sensor measurements can be made continuously and the eEMA coordinated with the concurrent record.
There are indications that interventions for weight management, addictive and other deleterious behaviors can provide sustained effectiveness. Multiple factors and behaviors need to be targeted together with timely, personally tailored interventions supported by actionable information. The interventions need to be maintained outside of clinic or group settings and respond to the changing needs of participants. Individualized interventions on multiple behaviors are most important with individuals who are refractory managing their weight (US Dept of Health and Human Services reports). With their rich User Interface and increasingly capable processors smartphones are able to not only gather eEMA information enhanced with objective context they can also bring immediately useful information back to participants in studies or clinical treatments.
The invention could be used in any situation in which a person or entity wishes to collect data from and on individuals who have agreed to provide that data. For example, this invention could be used by market researchers wishing to collect data from potential users of a new product.